6,149 research outputs found
Chinese Medicines for Cancer Treatment from the Metabolomics Perspective
Cancer is one of the most prevalent diseases all over the world with poor prognosis and the development of novel therapeutic strategies is still urgently needed. The large amount of successful experiences in fighting against cancer-like diseases with Chinese medicine has suggested it as a great source of alternative treatments to human cancers. Cancer cells have been shown to own a predominantly unique metabolic phenotype to facilitate their rapid proliferation. Metabolic reprogramming is a remarkable hallmark of cancer and therapies targeting cancer metabolism can be highly specific and effective. Based on the sophisticated study of small molecule metabolites, metabolomics can provide us valuable information on dynamically metabolic responses of living systems to certain environmental condition. In this chapter, we systematically reviewed recent studies on metabolism-targeting anticancer therapies based on metabolomics in terms of glucose, lipid, amino acid, and nucleotide metabolisms and other altered metabolisms, with special emphasis on the potential of metabolic treatment with pure compounds, herb extracts, and formulations from Chinese medicines. The trends of future development of metabolism-targeting anticancer therapies were also discussed. Overall, the elucidation of the underlying molecular mechanism of metabolism-targeting pharmacologic therapies will provide us a new insight to develop novel therapeutics for cancer treatment
CELLS: A Parallel Corpus for Biomedical Lay Language Generation
Recent lay language generation systems have used Transformer models trained
on a parallel corpus to increase health information accessibility. However, the
applicability of these models is constrained by the limited size and topical
breadth of available corpora. We introduce CELLS, the largest (63k pairs) and
broadest-ranging (12 journals) parallel corpus for lay language generation. The
abstract and the corresponding lay language summary are written by domain
experts, assuring the quality of our dataset. Furthermore, qualitative
evaluation of expert-authored plain language summaries has revealed background
explanation as a key strategy to increase accessibility. Such explanation is
challenging for neural models to generate because it goes beyond simplification
by adding content absent from the source. We derive two specialized paired
corpora from CELLS to address key challenges in lay language generation:
generating background explanations and simplifying the original abstract. We
adopt retrieval-augmented models as an intuitive fit for the task of background
explanation generation, and show improvements in summary quality and simplicity
while maintaining factual correctness. Taken together, this work presents the
first comprehensive study of background explanation for lay language
generation, paving the path for disseminating scientific knowledge to a broader
audience. CELLS is publicly available at:
https://github.com/LinguisticAnomalies/pls_retrieval
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Customizing Mems Designs via Conditional Generative Adversarial Networks
We present a novel systematic MEMS structure design approach based on a 'deep conditional generative model'. Utilizing the conditional generative adversarial network (CGAN) on a case study of circular-shaped MEMS resonators, three major advancements have been demonstrated: 1) a high-throughput vectorized MEMS design generation scheme that satisfies the geometric constraints; 2) MEMS structural customization toward tunable, desired physical properties with excellent generation accuracy; and 3) experience-free design space explorations to achieve extreme physical properties, such as low anchor loss of micro resonators. Excellent agreements with experimental data, numerical ulations, and a previously reported machine learning-based analyzer are achieved for validation of our methodology. As such, the proposed scheme could open up a new class of data-driven, intelligent design systems for a wide range of MEMS applications
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Trial-and-Error Learning for MEMS Structural Design Enabled by Deep Reinforcement Learning
We present a systematic MEMS structural design approach via a "trial-and-error"learning process by using the deep reinforcement learning framework. This scheme incorporates the feedback from each "trial"to obtain sophisticated strategies for MEMS design optimizations. Disk-shaped MEMS resonators are selected as case studies and three remarkable advancements have been realized: 1) accurate overall performance predictions (97.9%) via supervised learning models; 2) efficient MEMS structural optimizations to guarantee targeted structural properties with an excellent generation accuracy of 97.7%; and 3) superior design explorations to achieve one order of magnitude performance enhancement than the training dataset. As such, the proposed scheme could facilitate a wide spectrum of MEMS applications with this data-driven inverse design methodology
An enhanced convolutional neural network model for answer selection
© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License. Answer selection is an important task in question answering (QA) from the Web. To address the intrinsic difficulty in encoding sentences with semantic meanings, we introduce a general framework, i.e., Lexical Semantic Feature based Skip Convolution Neural Network (LSF-SCNN), with several optimization strategies. The intuitive idea is that the granular representations with more semantic features of sentences are deliberately designed and estimated to capture the similarity between question-answer pairwise sentences. The experimental results demonstrate the effectiveness of the proposed strategies and our model outperforms the state-of-the-art ones by up to 3.5% on the metrics of MAP and MRR
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Designing Weakly Coupled Mems Resonators with Machine Learning-Based Method
We demonstrate a design scheme for weakly coupled resonators (WCRs) by integrating the supervised learning (SL) with the genetic algorithm (GA). In this work, three distinctive achievements have been accomplished: 1) the precise prediction of coupling characteristics of WCRs with an accuracy of 98.7% via SL; 2) the stepwise evolutionary optimization of WCR geometries while maintaining their geometric connectivity via GA; and 3) the highly efficient generation of WCR designs with a mean coupling factor down to 0.0056, which outperforms 98% of random designs. The coupling behavior analysis and prediction are validated with experimental data of coupled microcantilevers from a published work. As such, this newly proposed scheme could shed light upon the structural optimization methods for high-performance MEMS devices with high degree of design freedom
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